Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines

Understand

While knowledge representation and reasoning are considered the keys for human-level artificial intelligence, connectionist networks have been shown successful in a broad range of applications due to their capacity for robust learning and flexible inference under uncertainty.

• The idea of representing symbolic knowledge in connectionist networks has been well-received and attracted much attention from research community as this can establish a foundation for integration of scalable learning and sound reasoning.
• In previous work, there exist a number of approaches that map logical inference rules with feed-forward propagation of artificial neural networks (ANN).
• However, the discriminative structure of an ANN requires the separation of input/output variables which makes it difficult for general reasoning where any variables should be inferable.